According to the Greek mythology, Typhon was a gigantic monster with one hundred dragon heads, bigger than all mountains. His open hands were extending from East to West, his head could reach the sky and flames were c...
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An overview of existing urban software mobile applications of the transport and economic direction is given. A model of a functional rationalizer of consumer behavior is being built. The software model of the function...
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In recent years, deep neural network (DNN) has been frequently used for classification. In this study, iris flowers having 3 different types are classified by using DNN which are utilized the width and length of petal...
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Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptom...
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Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptoms, varies from patient to another. Since the seizures are not predictable, and most of epileptic patients suffer from physical risky symptoms during the seizure, such patients are not able to perform daily work activities. The objective of this project is to design and implement a monitoring system for epileptic patients; the system should continuously check some vital signs, analyze the measurements, and decide whether the patient is nearly to have a seizure or not. Whenever a seizure is predicted, the system initiates an alarm. In addition, a notification should be sent to the health care responsible, as well as one preferred contact. By implementing the monitoring system, people who suffer from epilepsy will have more chance to work and live a normal life. Thus, this paper presents the concept of the overall system and shows results of the implemented systems: EEG, ECG and Fall Detection system. Results have shown that the fall detection accuracy reached 99.89% whereas the accuracy of the prediction using the ANN was about 97.34%.
We introduce the Piquasso quantum programming framework, a full-stack open-source software platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via a high-level Python ...
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This paper discusses the structural organization of the background monitoring system (BMS) of the transboundary UNESCO World Heritage Site “Beech forests of the Carpathians and ancient beech forests of Germany” usin...
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ISBN:
(数字)9781728167602
ISBN:
(纸本)9781728167619
This paper discusses the structural organization of the background monitoring system (BMS) of the transboundary UNESCO World Heritage Site “Beech forests of the Carpathians and ancient beech forests of Germany” using actual data and information from Gorgany Nature Reserve in Ivano-Frankivsk, Ukraine. The features of the structure and functions of background monitoring of the protected natural resources, forests and wildlife of Gorgany Nature Reserve is based on modern methods of collecting and processing of digital data, implementation of the principles of Internet of Things and cloud technologies, combined with GPS-systems, sensor networks with the use of optical and fiber-optic telecommunication channels. The information technology of the background monitoring system is based on figurative cluster models of monitoring objects according to theoretical principles of statistical, correlatory, spectral, cluster, logic-statistical and entropy analysis.
The process of extracting meaningful rules from big and complex data is called data mining. Data mining has an increasing popularity in every field today. Data units are established in customer-oriented industries suc...
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This paper describes the process of identifying the state of a computer system based on tracking changes in the file system under the condition that there is no reliable information about the state of objects in the e...
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ISBN:
(纸本)9781728122144
This paper describes the process of identifying the state of a computer system based on tracking changes in the file system under the condition that there is no reliable information about the state of objects in the external environment and the state of the system itself. In this case, the performance indicators of a computer system are random variables with unknown distribution laws. A method for identifying the state of the computer system based on tracking changes in the file system using classical discriminative analysis, taking into account the uncertainty of the input data, is proposed. For this purpose, a special method was developed for solving a fuzzy system of linear algebraic equations. The software was developed for tracking changes in the computer file system: creating, modifying, renaming, and deleting files and directories. The program also performs state classification based on a fuzzy discriminant classifier.
Measles is still one of the deadly diseases that terrify the world. Making accurate decisions is necessary in organizing an effective fight against the disease. Forecasting is a very important tool for planning future...
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Measles is still one of the deadly diseases that terrify the world. Making accurate decisions is necessary in organizing an effective fight against the disease. Forecasting is a very important tool for planning future needs of the health services including the vaccine stocks. Forecasting methods will help to watch over the critical stock levels of the medication as well as their expiration dates and perhaps take the necessary action to ensure a health stock flow. Governments and health institutions estimate the measles vaccine requirements using certain equations generally based on the size of the target population and the past consumption records. There are several studies that have examined the measles forecasting and conducted a vaccine requirement assessment. In this study genetic algorithm (GA) based trained recurrent fuzzy neural network (RFNN) and Adaptive neuro-fuzzy inference system (ANFIS) used to forecast the monthly measles cases in Ethiopia. The Ethiopia measles data was extracted from the World Health Organization Measles and Rubella Surveillance Data, which covers the period from January 2011 to December 2017. Out of total monthly measles cases, 80% were used for training, and 20% were chosen for testing. GA based trained RFNN shows better performance compared to ANFIS results.
The online tool travis can synthesize a k-bounded Petri net model from a reachability graph and unfold a k-bounded Petri net to its reachability graph. Synthesis is built on the theory of regions, but where travis and...
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